Abstract: 

CML is a project to help ML and data science practitioners automate their ML model training and model evaluation using best practices and tools from software engineering, such as GitLab CI/CD (as well as GitHub Actions and BitBucket Pipelines). The idea is to automatically train your model and test it in a production-like environment every time your data or code changes.

Session Outline:

In this talk, you’ll learn how to:
- Automatically allocate cloud instances (AWS, Azure, GCP) to train ML models. And automatically shut the instance down when training is over
- Automatically generate reports with graphs and tables in pull/merge requests to summarize your model’s performance, using any visualization library
- Transfer data between cloud storage and computing instances with DVC
- Customize your automation workflow with GitLab CI/CD

Bio: 

Alex Kim is a Solutions Engineer at Iterative. His background is in physics, software engineering, and machine learning. In the last couple of years, he became increasingly interested in the engineering side of ML projects: processes and tools needed to go from an idea to a production solution.